Information processing method, program, and recording medium

Abstract

This system estimates bottlenecks in students' learning across multiple subjects and supports their effective learning. [Solution] The student's account and the parent's account are managed by linking them to a single family identifier. Learning log data for each of the multiple subjects the student is taking, and message data related to the student's learning exchanged between the instructor, the student, and / or the parent are acquired. The acquired data is stored linked to the family identifier. The learning log data and message data linked to the family identifier are collected as integrated data, and bottlenecks in the student's learning are estimated based on the integrated data. In estimating bottlenecks, the learning items included in the integrated data are classified based on the skills required to acquire those learning items. The student's skills are evaluated based on the integrated data, and skills that the student lacks in common across multiple learning items in different subjects are estimated as bottlenecks.

Description

[[Technical Field]]

[0001] The present invention relates to an information processing method executed by a computer in an online learning service provided by connecting students and instructors via a network, a program that causes a computer to execute the information processing method, and a recording medium having the program recorded thereon. [[Background Art]]

[0002] Online learning services provided by connecting students and instructors via a network are becoming widespread. The user management system described in Patent Document 1 acquires learning history data such as a user's learning time, time required to answer an answer, correct answer rate, and number of times hints are viewed, and allows a student to determine whether the student can enjoy the effects of the learning service based on the learning history data. Evaluate whether the evaluation result is used, for example, for learning support for students by an instructor. [[Prior Art Documents]] [[Patent Documents]]

[0003] [[Patent Document 1]] Japanese Unexamined Patent Application Publication No. 2025-022633 [[Summary of the Invention]] [[Problems to be Solved by the Invention]]

[0004] When a student takes a plurality of subjects on an online learning service, the instructor may differ for each subject. When the instructor differs for each subject, each instructor is typically restricted from viewing learning history data of subjects handled by other instructors. In addition, when the online learning service includes a message function such as a chat that enables sending and receiving messages between the student, the student's guardian, and the instructor, each instructor typically cannot view messages sent and received between the student or the student's guardian and other instructors.

[0005] Incidentally, learning bottlenecks for students are not necessarily independent of each subject. For example, a lack of reading comprehension may be a common bottleneck in learning long passages in Japanese language and in learning word problems in mathematics. However, when different instructors are in charge of each subject, and the sharing of learning history and messages between instructors is limited, it is not easy to analyze bottlenecks across multiple subjects.

[0006] One of the objectives of this invention is to estimate bottlenecks in a student's learning across multiple subjects and to support the student's effective learning. [Means for solving the problem]

[0007] An information processing method according to one aspect of the present invention is an information processing method performed by a computer in an online learning service provided by connecting students and instructors via a network, Management steps include linking the student's account and the student's guardian's account to a single family identifier, A storage step which involves acquiring learning log data for each of the multiple subjects taken by the student, acquiring message data regarding the student's learning exchanged between the student and / or their guardian and multiple instructors who are each in charge of a portion of the multiple subjects, and storing the acquired learning log data and message data in association with the family identifier to which the student and their guardian belong. A collection step of collecting the learning log data and message data associated with the family identifier as integrated data, An estimation step to estimate bottlenecks in the learner's learning based on the integrated data, An output step that outputs information including the aforementioned bottleneck, Equipped with, The estimation step described above is: The learning items included in the aforementioned integrated data are classified based on the skills required to acquire those learning items. The skills of the participants are evaluated based on the integrated data, and the skills that the participants lack in common across multiple learning items in different subjects are estimated as bottlenecks. This includes the following.

[0008] Furthermore, a program according to one aspect of the present invention causes a computer to execute the above-described information processing method.

[0009] Furthermore, one embodiment of the present invention is a computer-readable recording medium on which the above-mentioned program is recorded. [Effects of the Invention]

[0010] According to the present invention, by linking learning log data and message data to family identifiers to which the learner and their guardian belong, data related to the learner's learning can be collected comprehensively and efficiently. Based on the collected data, by extracting skills that the learner lacks in common across multiple learning items in different subjects, bottlenecks in the learner's learning can be accurately estimated across multiple subjects. This makes it possible to support the learner's effective learning. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic diagram illustrating an example of a system configuration for explaining embodiments of the present invention. [Figure 2] Figure 1 is a flowchart showing an example of the process performed by the system's management server. [Figure 3] This flowchart shows another example of the processes performed by the management server of the system in Figure 1. [Figure 4] This is a schematic diagram illustrating another configuration example of the system for explaining embodiments of the present invention. [Modes for carrying out the invention]

[0012] The embodiments of the present invention will be described below. The embodiments described below are illustrative and the present invention is not limited thereto.

[0013] The system 100 shown in Figure 1 comprises a management server 101, a student terminal 102, a parent / guardian terminal 103, and multiple instructor terminals 104. The management server 101, student terminal 102, parent / guardian terminal 103, and multiple instructor terminals 104 can communicate with each other via network N. The management server 101 may be a cloud server or an on-premise server. Each of the student terminals 102, parent / guardian terminal 103, and instructor terminals 104 can be a smartphone, tablet, notebook PC, desktop PC, etc.

[0014] The management server 101 comprises a processor, a storage device such as memory, and a communication interface. The processor executes programs stored in the storage device to realize each function of the online learning service provided by system 100. Similarly, the student terminal 102, parent terminal 103, and instructor terminal 104 each comprise a processor, a storage device such as memory, and a communication interface, and further include an input / output interface. The input / output interface includes a keyboard, mouse, touchscreen, microphone, camera, display, etc.

[0015] System 100, as an online learning service, connects student terminals 102 and instructor terminals 104 via a network and provides a lecture environment using video calls including video and audio. The management server 101 acquires the video and audio data of the lecture and stores the acquired data in a storage device. The management server 101 may also generate transcript data from the audio data, and the generated transcript data is stored in the storage device.

[0016] The system 100 may provide, as an online learning service, an assignment management function including distributing assignments given by an instructor to students in accordance with learning items covered in lectures, receiving students' deliverables for the assignments, distributing corrected assignments after the instructor grades the deliverables, and the like. The management server 101 acquires assignment-related data such as assignments, deliverables, and corrected assignments, and stores the acquired data in a storage device.

[0017] The system 100 may provide a lecture reporting function as an online learning service. A lecture report is, for example, provided from an instructor to a student's guardian, and may include information such as learning items covered in the lecture, the progress of learning in the lecture, and the instructor's observations regarding the student's level of understanding in the lecture, and the like. The management server 101 acquires report data, and stores the acquired data in a storage device.

[0018] Furthermore, the system 100 provides, as an online learning service, a function for transmitting and receiving messages between students and instructors, and between students' guardians and instructors. Messages transmitted and received between the student and / or guardian and the instructor include, for example, questions to the instructor regarding the content of lectures or assignments, consultations with the instructor regarding how to proceed with learning, answers from the instructor to these questions and consultations, and may also be notifications regarding schedules such as rescheduling of a planned lecture. The management server 101 acquires message data, and stores the acquired data in a storage device.

[0019] An account database that manages accounts of each student, each student's guardian, and each instructor is stored in the storage device of the management server 101.

[0020] A student takes one or more subjects using the online learning service provided by the system 100. In the following description, it is assumed that the student takes a plurality of subjects, and a plurality of instructors share responsibility for the plurality of subjects. Note that instructors may differ per subject, and one instructor may be in charge of two or more subjects.

[0021] The management server 101 classifies lecture video and audio data, assignment-related data, and report data by subject and stores them in a storage device as learning log data for each subject. The management server 101 then links the learning log data to the student's account and the instructor's account, and controls access to the learning log data by account. Students can view the learning log data for all subjects they are taking. Instructors can view the learning log data for the subject they are teaching among the multiple subjects the student is taking, but are restricted from viewing the learning log data for subjects other than their own. The learning log data for some or all of the multiple subjects the student is taking may be linked to the student's parent's account, in which case the parent can also view some or all of the student's learning log data.

[0022] The management server 101 associates message data with sender and recipient accounts and controls access to message data by account. For example, message data between a student and an instructor is associated with the student's account and the instructor's account. Students and instructors can view this message data, but other instructors and the student's guardians are restricted from viewing it. Similarly, message data between a student's guardian and an instructor is associated with the guardian's account and the instructor's account. Guardians and instructors can view this message data, but other instructors and students are restricted from viewing it.

[0023] The management server 101 further links the student's account and the student's guardian's account to a single family identifier. In the data structure described above, the learning log data and message data are linked to the family identifier to which the student and guardian belong, via the student's account and the guardian's account.

[0024] The management server 101 comprehensively collects learning log data and message data related to the student and their guardian from storage devices using family identifiers. Then, based on the collected data (hereinafter referred to as integrated data), the management server 101 estimates bottlenecks in the student's learning and outputs information including the estimated bottlenecks.

[0025] The estimation of bottlenecks in learners' learning performed by the management server 101 includes classifying learning items included in the integrated data based on the skills required to acquire those learning items, evaluating learners' skills based on the integrated data, and extracting skills that learners lack in common across multiple learning items in different subjects.

[0026] Learning items can also be called learning units, and they are set appropriately for each subject. Learning items in Japanese language include, for example, narrative texts, argumentative texts, and short essays, and the skills necessary to acquire these learning items include, for example, vocabulary, reading comprehension, logical thinking, and expression. If we classify each learning item based on the skills necessary to acquire, narrative texts mainly require vocabulary and reading comprehension to analyze the scene, such as "when, where, who, and what happened," and to grasp the feelings of the characters in that scene. Argumentative texts mainly require vocabulary and logical thinking to connect the author's argument with evidence and examples. Short essays mainly require vocabulary, logical thinking, and expression.

[0027] Mathematics learning topics include, for example, numbers and expressions, geometry, and statistics. The skills necessary to master these topics include, for example, knowledge of theorems, calculation skills, reading comprehension, logical thinking skills, and analytical skills. If we classify each learning topic based on the skills required to master it, numbers and expressions primarily require calculation skills, and for word problems in numbers and expressions, reading comprehension skills to understand the questions are also necessary. Geometry primarily requires knowledge of theorems and other concepts, as well as logical thinking skills to prove the properties of geometric figures using this knowledge. Statistics primarily requires knowledge of indicators such as the mean, median, and mode, as well as analytical skills.

[0028] The above-mentioned classification based on learning items and the skills required to acquire them is merely an example and is not particularly limiting. Classifications based on learning items and the skills required to acquire them will be set appropriately for other subjects such as English, science, and social studies.

[0029] The skills of the students can be evaluated, for example, based on assignment-related data included in the learning log data collected as integrated data. As mentioned above, assignment-related data includes assignments given by the instructor to students in accordance with the learning items covered in the lecture, the students' deliverables for the assignments, and the instructor's corrections to the deliverables, and may further include additional information accompanying the assignments, deliverables, and corrections.

[0030] Additional information may include, for example, the amount of work assigned and the time students spent completing the assignments. The amount of work assigned may be given in different units (number of questions, number of pages, number of characters, etc.) depending on the type of work, or it may be converted using a conversion factor set according to the type and difficulty of the work. This conversion allows for a unified handling of the amount of work assigned, even if the types and difficulty levels of the work are different.

[0031] By using the amount of work assigned and the time spent completing it, learners' skills can be quantitatively evaluated. For example, by comparing the processing time per unit (calculated by dividing the time spent completing the work by the amount of work) across different learning items, learners' skills can be evaluated relatively. That is, if the processing time per unit for a particular learning item is relatively long, it can be said that the skills required to acquire this learning item are lower than the skills required to acquire other learning items. Furthermore, a standard processing time may be set based on past statistical data, etc., in addition to the processing time per unit, and learners' skills can be evaluated absolutely by comparing the processing time per unit with the standard processing time.

[0032] The additional information may further include information regarding the correct answer rate and / or resubmission of the assignment. The correct answer rate may be given, for example, as the ratio of correctly answered questions to the total number of questions, or as the score obtained relative to the score when all questions are answered correctly. Information regarding resubmissions may be, for example, the number of resubmissions or the number of questions that were subject to resubmission. By incorporating information regarding the correct answer rate and / or resubmissions into the processing time per unit, it becomes possible to consider the quality of assignment processing (carefulness, sloppiness) which can affect the length of processing time, and the accuracy of evaluating the learner's skills is improved.

[0033] Identifying the skills that students lack is done across multiple subjects. For example, if a student takes a long time to complete a narrative text assignment in Japanese language, which requires vocabulary and reading comprehension, it is difficult to pinpoint the cause in isolation—whether it is a lack of vocabulary specific to Japanese language, or / or a lack of reading comprehension. However, if the student also takes a long time to complete a word problem involving numbers and expressions in mathematics, which requires calculation and reading comprehension, then it is highly likely that the student lacks the reading comprehension skills common to both of these learning areas.

[0034] In this way, by extracting skills that are commonly lacking in students across multiple learning items in different subjects, it is possible to separate them from subject-specific skills such as vocabulary in Japanese language or theorems in mathematics, and instead identify deficiencies in fundamental skills that can affect learning in general, such as reading comprehension and logical thinking. Estimating these deficiencies in fundamental skills that can affect learning in general as bottlenecks in students' learning and working to resolve them will contribute to students' effective learning.

[0035] Participants' skills can also be evaluated based on transcript data, report data, and message data included in the learning log data. In evaluations based on this text data, for example, the text data may be processed using natural language processing to extract contextual information that suggests skill deficiencies.

[0036] For example, student questions regarding learning items and instructor answers to those questions may be extracted from transcript data and / or message data. If a student's question is along the lines of "I don't understand the meaning of the question," it may suggest a lack of reading comprehension. Also, if the same or similar questions and answers are repeated, it may suggest a lack of knowledge.

[0037] Furthermore, as mentioned above, the report data includes information such as the instructor's observations, and may contain information that directly or indirectly suggests the students' lack of skills in the learning items covered in the lecture. In addition, since the report is from the instructor to the students' parents, the questions that parents ask the instructor after receiving the report, and the instructor's answers to those questions, may also contain information that directly or indirectly suggests the students' lack of skills in the learning items. For example, if there are observations from instructors or parents that point out the students' lack of knowledge, or observations that point out their lack of reading comprehension, these observations may be extracted from the report data or message data.

[0038] By incorporating this contextual information into the assessment of learners' skills, the accuracy of the assessment improves. This leads to improved accuracy in identifying skills that learners lack across multiple learning items in different subjects, and thus improves the accuracy of estimating bottlenecks in learners' learning.

[0039] Participants' skills are not limited to skills related to learning content, such as reading comprehension and logical thinking, but may also include skills related to learning management, such as time management and the ability to execute plans. Management skills can be evaluated, for example, based on the submission status of assignments, and additional information in the assignment-related data may include information on the assignment deadline and the submission status against the deadline.

[0040] For example, if a student misses or fails to submit an assignment in one subject, it could simply be due to the student's carelessness. However, if this occurs in multiple subjects, it is highly likely that the student lacks learning management skills, such as time management and the ability to execute plans. Furthermore, learning management skills are fundamental skills that can affect learning in general. Therefore, assuming that a lack of management skills is a bottleneck in the student's learning and working to resolve it will contribute to the student's effective learning.

[0041] Furthermore, if assignments are missed or not submitted in multiple subjects, it is possible that the student is spending time on activities other than learning, such as using online learning services, and that their learning plan is being disrupted. Such time shortages due to external factors can be evaluated based on message data exchanged between the student or parent and the instructor. For example, contextual information about the student's schedule, such as "I'm busy with school club activities" or "I don't have time because it's the school's regular test period," can be extracted from the message data and incorporated into the evaluation of the student's skills.

[0042] For evaluating the skills of the participants, identifying skills that are lacking in participants across multiple learning items in different subjects, and estimating bottlenecks, machine learning models built using, for example, past learning log data may be used, or general-purpose or tuned generative artificial intelligence (Generative Artificial Intelligence) may be used.

[0043] Figure 2 shows an example of the processing performed by the management server 101. The management server 101 periodically collects integrated data (step S101) and estimates the learner's bottlenecks based on the integrated data (step S102). The management server 101 then outputs information including the estimated bottlenecks to the learner terminal 102 and / or the guardian terminal 103 (step S103). The form of output is not particularly limited, and for example, it may be automatically sent to the learner terminal 102 and / or the guardian terminal 103 in the form of a learning report for a predetermined period.

[0044] Figure 3 shows another example of the processing performed by the management server 101. The management server 101 collects integrated data in response to a request from the student or guardian (step S201) (step S202), and estimates the student's bottlenecks based on the integrated data (step S203). Furthermore, in this example, the student's schedule is extracted from the message data included in the integrated data (step S204), and a learning plan for the student is created based on the extracted schedule and the estimated bottlenecks, concerning at least one of the learning priorities and learning volume allocation for multiple subjects (step S205). A machine learning model or generative AI may be used to create the learning plan. The management server 101 then outputs information including the estimated bottlenecks and the created learning plan to the student terminal 102 and / or the guardian terminal 103 (step S206).

[0045] When a generative AI is used to estimate bottlenecks and create a learning plan, the messaging function included in the online learning service may provide chats between the student and the generative AI, and between the parent / guardian and the generative AI. In this case, the student's or parent's request may be entered into the management server 101 through a chat with the generative AI regarding the student's learning consultation. The student's schedule may also be provided to the generative AI through a chat. Furthermore, the estimated bottlenecks and the created learning plan may be presented to the student or parent / guardian through the chat in the form of a response generated by the generative AI.

[0046] If the estimated bottleneck stems from a lack of skills related to learning content, such as reading comprehension or logical thinking, a learning plan may be created that prioritizes learning items that contribute to strengthening the lacking skills, and allocates a greater amount of time to them. Along with the creation of the learning plan, requests for lectures on the priority learning items and requests for assignments may be sent from the management server 101 to the assigned instructor.

[0047] If the estimated bottleneck stems from a lack of learning management skills, such as time management ability or the ability to execute plans, a learning plan may be created that, for example, breaks down tasks into smaller steps and increases the number of points for instructor review. If a lack of time due to external factors is also estimated, a learning plan may be created for the student that adjusts at least one of the learning priorities and learning allocations for multiple subjects according to the student's schedule. For example, if a school periodic test is scheduled, a learning plan may be created that increases the learning priority of the test subject and / or reduces the learning allocation for subjects other than the test subject. A request to reduce the amount of assignments for subjects other than the test subject may be sent from the management server 101 to the assigned instructor.

[0048] The generating AI may be stored in the storage device of the management server 101 and run on the processor of the management server 101. Alternatively, the generating AI may run on the external server 105 as shown in Figure 4. When the generating AI runs on the external server 105, the management server 101 sends the collected integrated data and a prompt to the generating AI to perform a predetermined process to the external server 105. The management server 101 then receives the output of the generating AI from the external server 105.

[0049] The prompt causes the generating AI to perform at least the following processes: (A) classifying the learning items included in the integrated data based on the skills required to acquire those learning items; and (B) evaluating the learner's skills based on the integrated data and estimating the skills that the learner lacks in common across multiple learning items in different subjects as bottlenecks. The prompt may further cause the generating AI to perform the following processes: (C) extracting the learner's schedule from the message data included in the integrated data; and (D) creating a learning plan for the learner regarding at least one of the learning priorities and learning volume allocation for multiple subjects, based on the extracted schedule and the estimated bottlenecks.

[0050] This specification discloses at least the following:

[0051] [1] An information processing method performed by a computer in an online learning service provided by connecting students and instructors via a network, Management steps include linking the student's account and the student's guardian's account to a single family identifier, A storage step which involves acquiring learning log data for each of the multiple subjects taken by the student, acquiring message data regarding the student's learning exchanged between the student and / or their guardian and multiple instructors who are each in charge of a portion of the multiple subjects, and storing the acquired learning log data and message data in association with the family identifier to which the student and their guardian belong. A collection step of collecting the learning log data and message data associated with the family identifier as integrated data, An estimation step to estimate bottlenecks in the learner's learning based on the integrated data, An output step that outputs information including the aforementioned bottleneck, Equipped with, The estimation step described above is: The learning items included in the aforementioned integrated data are classified based on the skills required to acquire those learning items. The skills of the participants are evaluated based on the integrated data, and the skills that the participants lack in common across multiple learning items in different subjects are estimated as bottlenecks. Including, Information processing methods.

[0052] [2] The information processing method of [1] above, The learning log data includes assignments given to the student in accordance with the learning items and assignment-related data relating to the student's handling of those assignments. The evaluation of the participant's skills includes evaluating them based on the task-related data included in the integrated data. Information processing methods.

[0053] [3] The information processing method of [2] above, The aforementioned task-related data includes information regarding the quantity of the task and the time required to process the task. Information processing methods.

[0054] [4] The information processing method of [3] above, The aforementioned task-related data further includes information regarding the correct answer rate and / or resubmission of the aforementioned task. Information processing methods.

[0055] [5] The information processing method of [2] above, The aforementioned task-related data includes information regarding the submission deadline for the task and the submission status of the task against the deadline. Information processing methods.

[0056] [6] The information processing method of the above [1], The learning log data includes report data of lectures conducted between the student and the instructor, which is created by the instructor. The evaluation of the participant's skills includes evaluating them based on the report data included in the integrated data, Information processing methods.

[0057] [7] The information processing method of the above [1], The learning log data includes transcript data generated from audio data of lectures conducted between the student and the instructor. The evaluation of the participant's skills includes evaluating them based on the transcript data included in the integrated data. Information processing methods.

[0058] [8] The information processing method of the above [7], The evaluation of the participant's skills based on the aforementioned transcript data is as follows: The student's questions regarding the learning items and the instructor's answers to those questions are extracted from the transcript data. The evaluation will be based on the content of the extracted questions and answers. Including, Information processing methods.

[0059] [9] The information processing method of [1] above, The evaluation of the aforementioned participants' skills is as follows: The questions from the student and / or the guardian regarding the learning items, and the instructor's answers to those questions, are extracted from the message data included in the integrated data. The evaluation will be based on the content of the extracted questions and answers. Including, Information processing methods.

[0060]

[10] The information processing method of the above [1], The system further comprises a planning step of extracting the student's schedule from the message data included in the integrated data, and creating a learning plan for the student regarding at least one of the learning priorities and learning volume allocation for the multiple subjects based on the extracted schedule and the estimated bottlenecks, The information output in the output step further includes the created learning plan, Information processing methods.

[0061]

[11] The information processing method of [1] above, An input acceptance step for receiving input from the student or their guardian regarding the student's learning consultation, A generation step involves providing the received input and the estimated bottleneck to the generating AI, causing the generating AI to generate a response sentence to the learning consultation, Furthermore, The output step outputs the response statement. Information processing methods.

[0062]

[12] The information processing method of the above

[11] , The estimation step includes providing the generating AI with the integrated data and prompts to perform the following processes (A) and (B), causing the generating AI to estimate the bottleneck in the learner's learning. Information processing methods. (A) Classify the learning items included in the integrated data based on the skills required to acquire those learning items. (B) Evaluate the skills of the participants based on the integrated data and identify the skills that the participants lack in common across multiple learning items in different subjects.

[0063]

[13] The information processing method of

[11] or

[12] above, The system further comprises a planning step which extracts the student's schedule from the message data included in the integrated data and the input received in the input acceptance step, and creates a learning plan for the student regarding at least one of the learning priority and learning volume allocation for the multiple subjects based on the extracted schedule and the estimated bottleneck, The generation step involves providing the received input, the estimated bottleneck, and the created learning plan to the generating AI, causing the generating AI to generate a response sentence to the learning consultation. Information processing methods.

[0064]

[14] The information processing method of the above

[13] , The plan creation step includes providing the generating AI with the integrated data and prompts to perform the following processes (C) and (D), thereby causing the generating AI to create a learning plan for the student. Information processing methods. (C) Extract the participant's schedule from the message data included in the integrated data. (D) Based on the extracted schedule and the estimated bottleneck, a learning plan for the student is created regarding at least one of the learning priorities and learning allocation for the multiple subjects.

[0065]

[15] A program that causes a computer to execute the information processing method described in [1] above.

[0066]

[16] A computer-readable recording medium on which the program described in

[15] above is recorded. [Explanation of symbols]

[0067] 100 Systems 101 Management Server 102 Participant terminals 103 Parental device 104 Instructor terminal 105 External Server N Network

Claims

1. An information processing method performed by a computer in an online learning service provided by connecting students and instructors via a network, Management steps include linking the student's account and the student's guardian's account to a single family identifier, A storage step which involves acquiring learning log data for each of the multiple subjects taken by the student, acquiring message data regarding the student's learning exchanged between the student and / or their guardian and multiple instructors who are each in charge of a portion of the multiple subjects, and storing the acquired learning log data and message data in association with the family identifier to which the student and their guardian belong. A collection step of collecting the learning log data and message data associated with the family identifier as integrated data, An estimation step to estimate bottlenecks in the learner's learning based on the integrated data, An output step that outputs information including the aforementioned bottleneck, Equipped with, The estimation step described above is: The learning items included in the aforementioned integrated data are classified based on the skills required to acquire those learning items. The skills of the participants are evaluated based on the integrated data, and the skills that the participants lack in common across multiple learning items in different subjects are estimated as bottlenecks. Including, Information processing methods.

2. The information processing method according to claim 1, The learning log data includes assignments given to the student in accordance with the learning items and assignment-related data relating to the student's handling of those assignments. The evaluation of the participant's skills includes evaluating them based on the task-related data included in the integrated data. Information processing methods.

3. The information processing method according to claim 2, The aforementioned task-related data includes information regarding the quantity of the task and the time required to process the task. Information processing methods.

4. The information processing method according to claim 3, The aforementioned task-related data further includes information regarding the correct answer rate and / or resubmission of the aforementioned task, Information processing methods.

5. The information processing method according to claim 2, The aforementioned task-related data includes information regarding the submission deadline for the task and the submission status of the task against the deadline. Information processing methods.

6. The information processing method according to claim 1, The learning log data includes report data of lectures conducted between the student and the instructor, which is created by the instructor. The evaluation of the participant's skills includes evaluating them based on the report data included in the integrated data, Information processing methods.

7. The information processing method according to claim 1, The learning log data includes transcript data generated from audio data of lectures conducted between the student and the instructor. The evaluation of the participant's skills includes evaluating them based on the transcript data included in the integrated data. Information processing methods.

8. The information processing method according to claim 7, The evaluation of the participant's skills based on the aforementioned transcript data is as follows: The student's questions regarding the learning items and the instructor's answers to those questions are extracted from the transcript data. The evaluation will be based on the content of the extracted questions and answers. Including, Information processing methods.

9. The information processing method according to claim 1, The evaluation of the aforementioned participants' skills is as follows: The questions from the student and / or guardian regarding the learning items, and the instructor's answers to those questions, are extracted from the message data included in the integrated data. The evaluation will be based on the content of the extracted questions and answers. Including, Information processing methods.

10. The information processing method according to claim 1, The system further comprises a planning step of extracting the student's schedule from the message data included in the integrated data, and creating a learning plan for the student regarding at least one of the learning priorities and learning volume allocation for the multiple subjects based on the extracted schedule and the estimated bottlenecks, The information output in the output step further includes the created learning plan, Information processing methods.

11. The information processing method according to claim 1, An input acceptance step for receiving input from the student or their guardian regarding the student's learning consultation, A generation step in which the received input and the estimated bottleneck are given to the generating AI, and the generating AI generates a response sentence to the learning consultation, Furthermore, The output step outputs the response statement. Information processing methods.

12. The information processing method according to claim 11, The estimation step includes providing the generating AI with the integrated data and prompts to perform the following processes (A) and (B), causing the generating AI to estimate the bottleneck in the learner's learning. Information processing methods. (A) Classify the learning items included in the integrated data based on the skills required to acquire those learning items. (B) Evaluate the skills of the participants based on the integrated data and identify the skills that the participants lack in common across multiple learning items in different subjects.

13. The information processing method according to claim 11 or 12, The system further comprises a planning step which extracts the student's schedule from the message data included in the integrated data and the input received in the input acceptance step, and creates a learning plan for the student regarding at least one of the learning priority and learning volume allocation for the multiple subjects based on the extracted schedule and the estimated bottleneck, The generation step involves providing the received input, the estimated bottleneck, and the created learning plan to the generating AI, causing the generating AI to generate a response sentence for the learning consultation. Information processing methods.

14. The information processing method according to claim 13, The plan creation step includes providing the generating AI with the integrated data and prompts to perform the following processes (C) and (D), thereby causing the generating AI to create a learning plan for the student. Information processing methods. (C) Extract the participant's schedule from the message data included in the integrated data. (D) Based on the extracted schedule and the estimated bottleneck, create a learning plan for the student regarding at least one of the learning priority and learning volume allocation for the multiple subjects.

15. A program that causes a computer to execute the information processing method described in claim 1.

16. A computer-readable recording medium having the program described in claim 15 recorded on it.

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